arXiv Machine Learning
Oct 2

SeqLoRA: Bilevel Orthogonal Adaptation for Continual Multi-Concept Generation

SeqLoRA introduces a bilevel optimization framework for continual learning of text‑to‑image diffusion models, jointly optimizing LoRA factors while enforcing orthogonality between new and existing concept bases. The method achieves monotone descent, convergence guarantees, and a high‑probability bound on catastrophic forgetting that depends on residual interference energy. Experiments on Stable Diffusion with up to 101 concepts demonstrate improved identity preservation, minimal cross‑concept leakage, no need for a fusion step, and scalability beyond memory limits of fusion‑based approaches.

By Javad Parsa, Enis Simsar, Amir Joudaki, Thomas Hofmann, Andr\'e M. H. Teixeira
arXiv Machine Learning
Oct 9

Enabling Preference-driven Unlearning in Few-step Distilled Text-to-Image Diffusion Models

The paper presents a preference-driven unlearning framework for few-step distilled text-to-image diffusion models. It identifies that standard Direct Preference Optimization (DPO) fails on these models due to altered generation dynamics and proposes a modified formulation aligned with few-step properties. The method enables direct concept removal while preserving desirable capabilities, and is evaluated on identity, NSFW, and object-level unlearning tasks with strong forgetting and retention results.

By Gaurav Patel, Jun Fang, Greg Ver Steeg, Qiang Qiu, Sravan Sripada